A Metamodel for the Evolution of Evolution

PAUL Simon Andrews; Susan Stepney

doi:10.7551/978-0-262-33027-5-ch108

A Metamodel for the Evolution of Evolution

PAUL Simon Andrews, Susan Stepney

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

The ability of evolution to influence its own course in micro-organisms such as bacteria is a desirable property for computational systems. As a step towards exploiting this, we define a metamodel for the Evolution of Evolution. The metamodel is based on the concepts of structure and process, which are embodied together as the Machine. By describing different types Machines and structures we can capture a flexible metamodel to form the underpinnings for a new generation of evolutionary algorithms.

Original language	English
Title of host publication	European Conference of Artificial Life 2015
Publisher	MIT Press
Pages	621-628
ISBN (Print)	9780262330275
DOIs	https://doi.org/10.7551/978-0-262-33027-5-ch108
Publication status	Published - 2015

Access to Document

10.7551/978-0-262-33027-5-ch108

Cite this

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title = "A Metamodel for the Evolution of Evolution",

abstract = "The ability of evolution to influence its own course in micro-organisms such as bacteria is a desirable property for computational systems. As a step towards exploiting this, we define a metamodel for the Evolution of Evolution. The metamodel is based on the concepts of structure and process, which are embodied together as the Machine. By describing different types Machines and structures we can capture a flexible metamodel to form the underpinnings for a new generation of evolutionary algorithms.",

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AB - The ability of evolution to influence its own course in micro-organisms such as bacteria is a desirable property for computational systems. As a step towards exploiting this, we define a metamodel for the Evolution of Evolution. The metamodel is based on the concepts of structure and process, which are embodied together as the Machine. By describing different types Machines and structures we can capture a flexible metamodel to form the underpinnings for a new generation of evolutionary algorithms.

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